Special Issue "Energy Consumption Forecasting Using Machine Learning"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "J: State-of-the-Art Energy Related Technologies".

Deadline for manuscript submissions: 28 February 2022.

Special Issue Editor

Dr. Sasan Barak
E-Mail Website
Guest Editor
Southampton Business School, University of Southampton, Southampton SO16 7PP, UK
Interests: machine learning; artificial intelligence; energy forecasting; deep learning

Special Issue Information

Dear Colleagues,

Energy is vital to the development of any country. In recent decades, as living standards have risen, the global energy demand has increased exponentially, and the problem of energy shortages has become increasingly apparent. Therefore, an excellent energy supply management solution is essential. Energy supply management is based on region-specific forecasts of demand. Inaccurate forecasts of this demand not only lead to a significant amount of wasted energy but also higher operating costs for production.

Recent empirical studies have shown that using machine learning approaches combined with statistical learning methods can provide better performance than traditional statistical methods (Barak and Sadegh, 2016). For example, the hybrid of the recurrent neural network and exponential smoothing models by Smyl (2020) won first prize in the M4 time series forecasting competition (Makridakis, Spiliotis, and Assimakopoulos, 2018). Memarzadeh and Keynia (2021) combined wavelet transform with CNN and LSTM, respectively, and the results outperformed the single network model. Kim and Cho (2019) combined both CNN and LSTM neural networks in order to take advantage of their respective strengths.

Therefore, in this Special Issue, we would like to analyse the potential of using machine learning, especially deep learning models, and their improvement using statistical learning methods for energy forecasting.

The main challenge in energy forecasting is related to electricity data forecasting, but it can also concern green energy sources.

Dr. Sasan Barak
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • energy forecasting
  • deep learning
  • machine learning
  • statistical learning
  • hybrid model

Published Papers

This special issue is now open for submission.
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